
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     str		,    _lowerCamelCase					:     str		,    _lowerCamelCase					:     PreTrainedTokenizer		,    _lowerCamelCase					:     int		,    _lowerCamelCase					:     Optional[int] = None		,    )   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[int]       =   {}

							if train_file is not None:
														__UpperCamelCase      :  List[Any]       =   [train_file]
							if eval_file is not None:
														__UpperCamelCase      :  Optional[Any]       =   [eval_file]
							if test_file is not None:
														__UpperCamelCase      :  Optional[int]       =   [test_file]

							__UpperCamelCase      :  Dict       =   datasets.load_dataset("csv"		,    data_files=_lowerCamelCase)
							__UpperCamelCase      :  List[Any]       =   list(ds[list(files.keys())[0]].features.keys())
							__UpperCamelCase      :  List[str]       =   features_name.pop(_lowerCamelCase)
							__UpperCamelCase      :  Optional[Any]       =   list(set(ds[list(files.keys())[0]][label_name]))
							__UpperCamelCase      :  Union[str, Any]       =   {label: i for i, label in enumerate(_lowerCamelCase)}
							__UpperCamelCase      :  Dict       =   tokenizer.model_input_names
							__UpperCamelCase      :  List[Any]       =   {}

							if len(_lowerCamelCase) == 1:
														for k in files.keys():
																					__UpperCamelCase      :  Dict       =   ds[k].map(
																					    lambda _lowerCamelCase: tokenizer.batch_encode_plus(
																					        example[features_name[0]]		,    truncation=_lowerCamelCase		,    max_length=_lowerCamelCase		,    padding="max_length")		,    batched=_lowerCamelCase		,    )
							elif len(_lowerCamelCase) == 2:
														for k in files.keys():
																					__UpperCamelCase      :  str       =   ds[k].map(
																					    lambda _lowerCamelCase: tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])		,    truncation=_lowerCamelCase		,    max_length=_lowerCamelCase		,    padding="max_length"		,    )		,    batched=_lowerCamelCase		,    )

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					__UpperCamelCase      :  int       =   {k: v for k, v in ex.items() if k in input_names}
																					__UpperCamelCase      :  List[Any]       =   labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					__UpperCamelCase      :  Tuple       =   {k: v for k, v in ex.items() if k in input_names}
																					__UpperCamelCase      :  Tuple       =   labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					__UpperCamelCase      :  str       =   {k: v for k, v in ex.items() if k in input_names}
																					__UpperCamelCase      :  Optional[Any]       =   labelaid[ex[label_name]]
																					yield (d, label)

							__UpperCamelCase      :  Tuple       =   (
							    tf.data.Dataset.from_generator(
							        _lowerCamelCase		,    ({k: tf.intaa for k in input_names}, tf.intaa)		,    ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))		,    )
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														__UpperCamelCase      :  List[Any]       =   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

							__UpperCamelCase      :  int       =   (
							    tf.data.Dataset.from_generator(
							        _lowerCamelCase		,    ({k: tf.intaa for k in input_names}, tf.intaa)		,    ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))		,    )
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														__UpperCamelCase      :  Dict       =   val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

							__UpperCamelCase      :  List[Any]       =   (
							    tf.data.Dataset.from_generator(
							        _lowerCamelCase		,    ({k: tf.intaa for k in input_names}, tf.intaa)		,    ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))		,    )
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														__UpperCamelCase      :  Optional[Any]       =   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

							return train_ds, val_ds, test_ds, labelaid


lowercase    :   Optional[Any]						=			logging.getLogger(__name__)




@dataclass
class 					lowerCamelCase__				:




	'''simple docstring'''




	_A								=	field(metadata={'help': 'Which column contains the label'})
	_A								=	field(default=__lowercase	,		metadata={'help': 'The path of the training file'})
	_A								=	field(default=__lowercase	,		metadata={'help': 'The path of the development file'})
	_A								=	field(default=__lowercase	,		metadata={'help': 'The path of the test file'})
	_A								=	field(
	    default=1_2_8	,		metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }	,		)
	_A								=	field(
	    default=__lowercase	,		metadata={'help': 'Overwrite the cached training and evaluation sets'})




@dataclass
class 					lowerCamelCase__				:




	'''simple docstring'''




	_A								=	field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
	_A								=	field(
	    default=__lowercase	,		metadata={'help': 'Pretrained config name or path if not the same as model_name'})
	_A								=	field(
	    default=__lowercase	,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
	_A								=	field(default=__lowercase	,		metadata={'help': 'Set this flag to use fast tokenization.'})
	# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
	# or just modify its tokenizer_config.json.
	_A								=	field(
	    default=__lowercase	,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,		)




def       _SCREAMING_SNAKE_CASE   (						)   ->       Tuple:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Optional[Any]       =   parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir)
							    and os.listdir(training_args.output_dir)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
														    " --overwrite_output_dir to overcome.")

							# Setup logging
							logging.basicConfig(
							    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"		,    datefmt="%m/%d/%Y %H:%M:%S"		,    level=logging.INFO		,    )
							logger.info(
							    F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, '
							    F'16-bits training: {training_args.fpaa}')
							logger.info(F'Training/evaluation parameters {training_args}')

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							__UpperCamelCase      :  Dict       =   AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,    cache_dir=model_args.cache_dir		,    )

							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  str       =   get_tfds(
							    train_file=data_args.train_file		,    eval_file=data_args.dev_file		,    test_file=data_args.test_file		,    tokenizer=_lowerCamelCase		,    label_column_id=data_args.label_column_id		,    max_seq_length=data_args.max_seq_length		,    )

							__UpperCamelCase      :  Tuple       =   AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,    num_labels=len(_lowerCamelCase)		,    labelaid=_lowerCamelCase		,    idalabel={id: label for label, id in labelaid.items()}		,    finetuning_task="text-classification"		,    cache_dir=model_args.cache_dir		,    )

							with training_args.strategy.scope():
														__UpperCamelCase      :  int       =   TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path		,    from_pt=bool(".bin" in model_args.model_name_or_path)		,    config=_lowerCamelCase		,    cache_dir=model_args.cache_dir		,    )

							def compute_metrics(_lowerCamelCase					:     EvalPrediction) -> Dict:
														__UpperCamelCase      :  Union[str, Any]       =   np.argmax(p.predictions		,    axis=1)

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							__UpperCamelCase      :  Dict       =   TFTrainer(
							    model=_lowerCamelCase		,    args=_lowerCamelCase		,    train_dataset=_lowerCamelCase		,    eval_dataset=_lowerCamelCase		,    compute_metrics=_lowerCamelCase		,    )

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir)

							# Evaluation
							__UpperCamelCase      :  str       =   {}
							if training_args.do_eval:
														logger.info("*** Evaluate ***")
														__UpperCamelCase      :  Optional[Any]       =   trainer.evaluate()
														__UpperCamelCase      :  Union[str, Any]       =   os.path.join(training_args.output_dir		,    "eval_results.txt")

														with open(_lowerCamelCase		,    "w") as writer:
																					logger.info("***** Eval results *****")

																					for key, value in result.items():
																												logger.info(F'  {key} = {value}')
																												writer.write(F'{key} = {value}\n')

																					results.update(_lowerCamelCase)

							return results


if __name__ == "__main__":
		main()

def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int = 10**9)   ->       int:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   1
							__UpperCamelCase      :  Any       =   2

							__UpperCamelCase      :  Dict       =   0
							__UpperCamelCase      :  Any       =   0
							__UpperCamelCase      :  List[Any]       =   0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														__UpperCamelCase      :  Optional[int]       =   2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
		print(f"{solution() = }")